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Using Evolutionary Functional-Structural Plant Modelling to Understand the Effect Of 1Using evolutionary functional-structural plant modelling to understand the effect of 2climate change on plant communities 1* 3Jorad de Vries 1 4 ETH Zürich, Institute for Integrative Biology, Zürich, Switzerland 5* Correspondence: [email protected] 6 7Abstract 8The “holy grail” of trait-based ecology is to predict the fitness of a species in a particular 9environment based on its functional traits, which has become all the more relevant in the light 10of global change. However, current ecological models are ill-equipped for this job: they rely 11on statistical methods and current observations rather than the mechanisms that determine 12how functional traits interact with the environment to determine plant fitness, meaning that 13they are unable to predict ecological responses to novel conditions. Here, I advocate the use 14of a 3D mechanistic modelling approach called functional-structural plant (FSP) modelling in 15combination with evolutionary modelling to explore climate change responses in natural 16plant communities. Gaining a mechanistic understanding of how trait-environment 17interactions drive natural selection in novel environments requires consideration of individual 18plants with multidimensional phenotypes in dynamic environments that include abiotic 19gradients and biotic interactions, and their combined effect on the different vital rates that 20determine plant fitness. Evolutionary FSP modelling explicitly simulates the trait- 21environment interactions that drive eco-evolutionary dynamics from individual to community 22scales and allows for efficient navigation of the large, complex and dynamic fitness 23landscapes that emerge from considering multidimensional plants in multidimensional 24environments. Using evolutionary FSP modelling as a tool to study climate change responses 25of plant communities can further our understanding of the mechanistic basis of these 26responses, and in particular, the role of local adaptation, phenotypic plasticity, and gene flow. 1 27Introduction 28The “holy grail” of trait-based ecology is to predict the fitness of a species in a particular 29environment based on its functional traits (Lavorel and Garnier 2002), which has become all 30the more relevant in the light of global change (Funk et al. 2017). The persistence of many 31plant species relies on their ability to either adapt to changing conditions in their current 32habitat range, or to track their climatic niche beyond their current range and into previously 33unoccupied habitats. Either way, these plant species will face an array of novel abiotic and 34biotic conditions that exert selection pressures not experienced within their current range 35(Franks et al. 2007, Franks and Weis 2008, Lustenhouwer et al. 2018). Predicting how plant 36populations and communities will respond to the novel conditions caused by climate change 37has thus far been challenging, because the roles of key eco-evolutionary mechanisms such as 38adaptive evolution, phenotypic plasticity and gene flow are still poorly understood (Anderson 39and Song 2020). Furthermore, current ecological models are ill-equipped to predict 40ecological responses to novel conditions due to their reliance on statistical methods and 41current observations (Pagel and Schurr 2012) rather than the mechanisms that determine how 42functional traits interact with the environment, thereby determining plant fitness (Williams 43and Jackson 2007, Angert et al. 2011, Alexander et al. 2016, Urban et al. 2016). This calls for 44the development of novel mechanistic modelling approaches designed to make predictions 45and formulate hypotheses on the adaptive value of functional traits and life-history strategies 46in a changing world (Urban et al. 2016). Here, I will advocate the utilisation of a 3D 47mechanistic modelling approach called functional-structural plant (FSP) modelling (Evers et 48al. 2018, Louarn and Song 2020), in combination with evolutionary modelling to explore 49climate change responses of natural plant communities. First, I will explain why 50understanding climate change responses of plant communities requires mechanistic 51modelling approaches. Second, I will introduce FSP modelling and discuss how FSP models 2 52can link individual plants to their (a)biotic environment to accurately simulate the trait- 53environment interactions that drive climate change responses of individual plants. Thirds, I 54will discuss how coupling FSP and evolutionary models allows scaling from individuals to 55communities through mechanistic simulation of demographic and evolutionary processes. 56Fourth, I will discuss how evolutionary FSP modelling can help explore the behaviour of 57complex systems with multidimensional plant phenotypes in multidimensional environments. 58Last, I will highlight the importance of considering the spatial and temporal dynamics of 59these multidimensional environments, their effects on selection, and the role of phenotypic 60plasticity. 61Understanding climate change responses requires mechanistic modelling approaches 62Climate change responses of plant communities are not dominated by any one trait or 63environmental factor, but rather, are the product of interactions between multiple traits, 64abiotic factors, biotic interactions, and demographic processes (McGill et al. 2006, Laughlin 65and Messier 2015). Plant communities are complex mixtures of different species that 66represent a range of functional strategies. In turn, each species within a community has 67functional trait variation, which affect the different vital rates that determine the fitness of 68that species (Laughlin et al. 2020). While functional traits are a great tool to describe 69variation in functional strategies on the species and community levels (Wright et al. 2004, 70Díaz et al. 2016), they have proven to be poor predictors of ecosystem functioning (van der 71Plas et al. 2020). This indicates that species level functional trait variation may fail to capture 72the granularity required to accurately link traits to vital rates, and that traits taken out of the 73context of the individual lack predictive power (Yang et al. 2018). This is highlighted by two 74observations: first, the fact that multiple functional strategies can coexist in a given 75environment challenges the idea that traits can predict ecosystem level responses in a detailed 76way (Adler et al. 2014). Second, functional trait variation on the species level is often the 3 77result of multiple populations that have adapted to their local environmental conditions, 78showing that functional traits variation needs to be considered in the context of the local 79environment. This population level variation is important to consider in the context of climate 80change, as it can either improve or impede a species’ ability to track their environmental 81niche or adapt to local environmental change (Atkins and Travis 2010, Anderson and Song 822020, Anderson and Wadgymar 2020). Observed patterns of local adaptation along 83environmental gradients suggest that populations at the species’ cold range edge are generally 84adapted to abiotic conditions, while populations at the warm range edge are generally adapted 85to biotic interactions (Griffith and Watson 2005, Hargreaves et al. 2014). These observations 86are in accordance with ecological theory, which suggests that the selection pressures exerted 87by abiotic conditions play a larger role in environments that are abiotically stressful, while 88biotic interactions play a larger role in more benign environments (Louthan et al. 2015, 89Briscoe Runquist et al. 2020). However, how abiotic and biotic selection pressures interact to 90shape locally adapted phenotypes is not clear-cut (Briscoe Runquist et al. 2020, Hargreaves et 91al. 2020), yet these interactions are key to understanding climate change impacts on plant 92communities (HilleRisLambers et al. 2013). Therefore, understanding how plant communities 93respond to the novel conditions resulting from climate change requires a focus on the 94mechanisms that link the functional traits to fitness on the level of individual plants through 95interactions with their abiotic and biotic environment. 96FSP modelling: linking the plant to their (a)biotic environment 97Perhaps the most important mechanism that links plant form and function to plant fitness in 98relation to its local (a)biotic environment is the acquisition of, and competition for resources 99such as light, water and nutrients, which shape plant communities through niche 100differentiation and competitive exclusion (Kunstler et al. 2016, Levine et al. 2017, Adler et 101al. 2018). Plant community structure is expected to change as a result of climate change, 4 102either through changes in the interactions between competitors that currently co-exist, or 103through the introduction of novel competitors. This leads to major changes in the identity and 104strength of competitive interactions, as well as the environmental context in which these 105interactions occur (Alexander et al. 2015). Predicting how these changes in competitive 106interactions may affect future plant communities requires modelling approaches to simulate 107the mechanisms of resource acquisition and competition, and their effects on demographic 108processes (Alexander et al. 2016). Competitive interactions are strong drivers of selection, 109because traits that favour resource acquisition may allow pre-emptive access to that resource 110(i.e. a tall plant shades a short plant but not vice
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